Big Data analytics is big business these days – more and more
firms want to be able to grasp key trends across huge datasets as
fast as possible. What was once interest from afar is now certainly
demand for the larger enterprises around. Obviously, given that the
Big Data revolution has yet fully realise the potential with
real-time analytics, simply because that capability doesn’t exist
within the batch-oriented Hadoop currently. And that functionality
isn’t coming for the forseeable future either.

However there are a number of options appearing on the horizon
seeking to change this. The latest to spot room in the market
is Nodeable, who have
diversified radically away from their cloud analytic systems
manager, which
they launched last year, towards a real-time model. Their
banner product is StreamReduce – the first real-time Hadoop
pre-processing engine that aims to make sense of the reams of
multi-structured data as it hits the system.

Based on open source Storm, a real-time analytics
framework that Twitter acquired from BackType last year
and now uses internally, StreamReduce is essentially a cloudy
version of its predecessor, yet is an important step to be made for
Big Data. It finally opens the doors to the wider developing
community, a move which Nodeable are keen to shout about to gain
appeal. Now, sophisticated data analytics are not just available to
data scientists but for those who aren’t so eager to shell out huge
capital to get their hands on critical insight. Pricing is set at
$99 per month.

Nodeable say that StreamReduce is the ideal ‘cloud-based
complement to batch processing via Apache Hadoop and Amazon
Web Services Elastic Map Reduce’, providing a much-needed speed
boost to MapReduce. Something which is badly needed with murmurs of
discontent about and are already getting glances from suitors. The
most notable so far is Cloudera.

Mike Olson, CEO of Cloudera and also on the board at Nodeable
said: “As the demand for Hadoop and big data insights
continues to grow, Nodeable’s pre-processing engine helps large
enterprises get more from Hadoop faster and its real-time cloud
delivery brings the benefits of big data analytics to companies
without the dedicated IT staff and resources of the Global
2000.”

“For almost a year the engineering team at Nodeable has worked
closely with more than 400 beta users who’ve told us that a
real-time analytics complement to Hadoop is a top priority,” said
Dave Rosenberg, Founder and CEO of Nodeable. “Batch workflows are
too slow for turning data into useful, actionable information.
StreamReduce solves that problem with a simple cloud-based
solution.”

The UI for StreamReduce looks familiar to those who’ve already
used the original Nodeable product, coming with a nice Twitter-like
gloss. SteamReduce runs in the Amazon Web Services cloud, picking
the best of AWS tools, as well as other popular NoSQL choices like
MongoDB and Amazon’s DynamoDB. Rosenberg however has stated to
GigaOM that this might change to Cassandra to suit higher volumes
of traffic.

The ideal use cases for StreamReduce according to Nodeable are
pretty broad, and include:

Log and clickstream analysis

Anomaly detection in Amazon Web Services EC2 instances

Security and fraud detection

Mobile and geo-location measurement

Pinpointed advertising and marketing

The key here is Hadoop might be have eagle-eye accuracy with
data that has been processed, but isn’t capable of harvesting
similar insights from data there and then. Thankfully, up steps
Nodeable with StreamReduce with a compelling solution to this
complex issue at least

Expect more real-time analytics companies crop up with their own
tool and buddy up with the Hadoop providers in the next few months.
However, Nodeable and Cloudera have gained a potentially pivotal
head start already on their respective competitors